High Dimensional Time Series Analysis Tools
Identifying the cointegration rank of nonstationary vector time series
Estimating the matrix time series CP-factor model
Generating simulated data for the example in Chang et al. (2024)
Factor analysis for vector time series
Fama-French 10*10 return series
Factor analysis with observed regressors for vector time series
HDTSA: High Dimensional Time Series Analysis Tools
Testing for martingale difference hypothesis in high dimension
Principal component analysis for vector time series
Make predictions from a "factors" object
Make predictions from a "mtscp" object
Make predictions from a "tspca" object
Multiple testing with FDR control for spectral density matrix
Global testing for spectral density matrix
Testing for unit roots based on sample autocovariances
U.S. Industrial Production indices
Testing for white noise hypothesis in high dimension
An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, cointegration analysis proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022) <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>, CP-decomposition for matrix time series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and Chang et al. (2024) <doi:10.48550/arXiv.2410.05634>, and statistical inference for spectral density matrix proposed by Chang et al. (2022) <doi:10.48550/arXiv.2212.13686>.